Business Intelligence / Analytics Manager (Hybrid)

Title: Business Intelligence / Analytics Manager

Location: Wilmington, DE(Hybrid)

Duration: Contract to Hire

Rate: $Open

Position Description:

• Manage the day to day functions of a team of Data Analyst & Business Intelligence professionals including distributing assignments and ensuring quality, accuracy and productivity of the unit s performance in accordance with the organization s policies and applicable laws.
• Manage and oversee the day-to-day functions of Variable Pay administration and reporting to include plan design, goal management, associate changes, and leveraging pay for performance software.
• Develop and implement processes, procedures, and standards that optimize resource efficiency while reducing cycle time.
• Responsible for extracting, building, refining and translating data and insights into strategy such that the interpretation of trends or patterns and identified solutions meet long-term business objectives.
• Provide feedback on plan performance (expenses paid compared to goal achievement and plan modeling) VPP-related business goals to business line owners regarding the performance of VPPs specific to their business.
• Create and deliver presentations and documentation that communicate complex analytical results in a manner that clearly conveys recommendations and/or answers to strategic questions, concerns, risks, and opportunities, designed to influence key stakeholders and drive strategic business decisions.
• Ensure standardization of the Business Intelligence unit including the use of key analytics processes, design of analytical approaches, and development of tools which enable benefit identification, analysis and reporting.
• Work with divisional leader to develop and implement a long-term vision and plan for Market and Business Analytics that includes resource and technology requirements, strategic goal alignment and long-term planning.
• Identify and oversee opportunities to collect and integrate data from disparate databases to drive business intelligence.
• Utilize analysis to influence key stakeholders and drive the direction of key issues that impact contact strategy, targeting, segmentation, offer, creative positioning, customer LTV and ROI.
• Develop a self-service environment whereby access to data is not solely kept in the hands of the business intelligence team.
• Lead the development and implementation of enterprise business intelligence capabilities to improve knowledge of customer behaviors to enable deployment of engagement strategies that positively impact ROI across divisions, optimization of marketing channels and rapid translation of learning into actionable opportunities to improve response rates, sales effectiveness and operational efficiencies.
• Develop and implement marketing operation processes, procedures, and standards that optimize resource efficiency while reducing cycle time.
• Responsible for the creation of Member Profiles, Model building, and other strategic models (including Risk of Flight implementation) to drive the business.
• Coordinate with other departments the best method and process to handle the enterprise needs for data analysis and overall database management efficiency including implementation of the models.

Required Skills:

• S Degree in Economics, Mathematics, statistics Management Information Systems or Computer Information Technology required, Equivalent combination of education and experience will also be considered.
• Detailed knowledge of business operations fundamentals with a strong understanding of underlying statistical methods.
• 7 years working experience in the Business Intelligence and analytics landscape with a specific focus on marketing, marketing data warehousing and/or database marketing/CRM.
• 5 years managing resources in a high performing database marketing or business intelligence environment.
• BASE SAS and SQL Microsoft Technologist certifications required.
• Proficiency in Microsoft Access, SAS Enterprise Guide and SAS Enterprise Miner.
• Working knowledge of statistical principles such as linear regression, logistic regression, univariate and multivariate analysis, and experimental design